KOtBu-catalysed α-homoallylic alkylation of acyclic amides with 1-aryl-1,3-dienes.

Herein, we report a KOtBu-catalysed α-homoallylic alkylation of acyclic amides with 1-aryl-1,3-dienes. With this transition metal-free and atom-economic protocol, a series of α-homoallylic alkylated acyclic amides were synthesized in good to excellent yields. This transformation is proposed to proceed through a cation-π interaction-based C-C bond formation from the in situ-generated potassium enolate with the diene unit.

Repairing Behaviors of Cracked Steel Plates Based on Bolted Fiber-Reinforced Polymer Plates.

The use of FRP materials to repair cracked/damaged steel structures has gradually been adopted by researchers. This paper investigates the repairing effect of bolted FRP plates for cracked steel plates based on experimental and numerical simulation methods. In the experimental investigation, the tensile strengths of six specimens, including three repaired specimens and three pure cracked steel specimens, were evaluated. The test outcomes indicated that the bolt repairing method significantly enhanced the tensile strengths of the cracked steel plates. As an example, the failure of a pure steel plate with a 1 mm width crack occurred at 813 N, whereas after being repaired, a tensile strength of 1298 N was observed. Based on finite element (FE) analysis, the influence of bolt preloads and interfacial friction coefficients were verified. The stress-relative ratio for specimens was contingent on the bolt preload magnitude and gradually decreased as the preload was augmented. By exploring the repairing effect for varied friction coefficients, it was concluded that using a higher bolt preload can aid in eliminating the performance discrepancy of the overall component caused by interface treatment errors.

Prior-Attention Residual Learning for More Discriminative COVID-19 Screening in CT Images.

We propose a conceptually simple framework for fast COVID-19 screening in 3D chest CT images. The framework can efficiently predict whether or not a CT scan contains pneumonia while simultaneously identifying pneumonia types between COVID-19 and Interstitial Lung Disease (ILD) caused by other viruses. In the proposed method, two 3D-ResNets are coupled together into a single model for the two above-mentioned tasks via a novel prior-attention strategy. We extend residual learning with the proposed prior-attention mechanism and design a new so-called prior-attention residual learning (PARL) block. The model can be easily built by stacking the PARL blocks and trained end-to-end using multi-task losses. More specifically, one 3D-ResNet branch is trained as a binary classifier using lung images with and without pneumonia so that it can highlight the lesion areas within the lungs. Simultaneously, inside the PARL blocks, prior-attention maps are generated from this branch and used to guide another branch to learn more discriminative representations for the pneumonia-type classification. Experimental results demonstrate that the proposed framework can significantly improve the performance of COVID-19 screening. Compared to other methods, it achieves a state-of-the-art result. Moreover, the proposed method can be easily extended to other similar clinical applications such as computer-aided detection and diagnosis of pulmonary nodules in CT images, glaucoma lesions in Retina fundus images, etc.